LINC01018 and SMIM25 sponged miR-182-5p in endometriosis revealed by the ceRNA network construction
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The current study intended to explore the interaction of the long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) under the background of competitive endogenous RNA (ceRNA) network in endometriosis (EMs). The differentially expressed miRNAs (DEmiRs), differentially expressed lncRNA (DELs), and differentially expressed genes (DEGs) between EMs ectopic (EC) and eutopic (EU) endometrium based on three RNA-sequencing datasets (GSE105765, GSE121406, and GSE105764) were identified, which were used for the construction of ceRNA network. Then, DEGs in the ceRNA network were performed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analysis. Besides, the DEmiRs in the ceRNA network were validated in GSE124010. And the target DELs and DEGs of verified DEmiRs were validated in GSE86534. The correlation of verified DEmiRs, DEGs, and DELs was explored. Moreover, gene set enrichment analysis (GSEA) was applied to investigate the function of verified DEmiRs, DEGs, and DELs. Overall, 1352 DEGs and 595 DELs from GSE105764, along with 27 overlapped DEmiRs between GSE105765 and GSE121406, were obtained. Subsequently, a ceRNA network, including 11 upregulated and 16 downregulated DEmiRs, 7 upregulated and 13 downregulated DELs, 48 upregulated and 46 downregulated DEGs, was constructed. The GO and KEGG pathway analysis showed that this ceRNA network probably was associated with inflammation-related pathways. Furthermore, hsa-miR-182-5p and its target DELs (LINC01018 and SMIM25) and DEGs (BNC2, CHL1, HMCN1, PRDM16) were successfully verified in the validation analysis. Besides, hsa-miR-182-5p was significantly negatively correlated with these target DELs and DEGs. The GSEA analysis implied that high expression of LINC01018, SMIM25, and CHL1, and low expression of hsa-miR-182-5p would activate inflammation-related pathways in endometriosis EU samples. LINC01018 and SMIM25 might sponge hsa-miR-182-5p to upregulate downstream genes such as CHL1 to promote the development of endometriosis.Keywords:
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The current study aims to investigate differences in whey protein of breastmilk of volunteered mother collected from two ethnic groups (Korean and Han) in China using data-independent acquisition (DIA) based proteomics technique. The total detected 624 proteins were principally allocated to cellular process of biological process (BP), cell and cell part of cell component (CC) and binding of molecular function (MF) according to Gene Ontology (GO) annotation; and carbohydrate metabolism of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Among the 54 differently expressed proteins, 8 were related with immunity. Enrichment data showed that intracellular of GO functions and viral myocarditis of KEGG pathways were most significantly enriched (p < 0.05). Protein-protein interaction (PPI) network suggested that 40S ribosomal protein S27a and 60S ribosomal protein L10a which interacted most with other proteins ranked the top two hub proteins by MCC (Maximal Clique Centrality) method. This study may have guiding role for development of infant formula powder for specific infants of Han or Korean groups according to responding breastmilk composition.
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This article presents data on genes associated with cleft palate (CP), retrieved through both a full-text systematic review and a mouse genome informatics (MGI) database search. In order to group CP-associated genes according to function, pathway, biological process, and cellular component, the genes were analyzed using category enrichment bioinformatics tools, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). This approach provides invaluable opportunities for the identification of candidate pathways and genes in CP research.
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Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and mostly occurs in viral hepatitis endemic areas such as China. Knowledge of HCC-related genes may lead to an early detection of HCC and develop molecularly targeted therapeutics, reducing mortality and improving a patient’s prognosis significantly. Therefore, it is valuable and important for us to identify common characters of HCC related genes. In this study, we proposed a computational method to predict HCC related genes based on Gene Ontology terms and KEGG terms using Random Forest (RF), in which features were optimized by maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). 224 HCC gene candidates were compiled from some databases, while 11,200non-HCC gene candidates were randomly selected from Ensemble database. 10 candidate datasets were constructed by dividing non-HCC gene candidates into 10 groups. Each gene in datasets was encoded by 13,126 features including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 615 GO terms and 11 KEGG pathways was discovered. Through analysis, we found these features were closely related to HCC, which means our method is effective for discovering HCC related genes, and it is hopeful that it can also be used to predict and analyze genes for other types of cancer. Keywords: Gene ontology, hepatocellular carcinoma (HCC), incremental feature selection (IFS), KEGG, maximum relevance minimum redundancy (mRMR), random forest (RF).
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A Pomegranate Peel Extract (PGE) has been proposed as a natural antifungal substance with a wide range of activity against plant diseases. Previous studies showed that the extract has a direct antimicrobial activity and can elicit resistance responses in plant host tissues. In the present study, the transcriptomic response of orange fruit toward PGE treatments was evaluated. RNA-seq analyses, conducted on wounded fruits 0, 6, and 24 h after PGE applications, showed a significantly different transcriptome in treated oranges as compared to control samples. The majority (273) of the deferentially expressed genes (DEGs) were highly up-regulated compared to only 8 genes that were down-regulated. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis showed the involvement of 1233 gene ontology (GO) terms and 35 KEGG metabolic pathways. Among these, important defense pathways were induced and antibiotic biosynthesis was the most enriched one. These findings may explain the underlying preventive and curative activity of PGE against plant diseases.
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Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.
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Rhinitis is a disorder of the nasal mucosa with inflammatory responses, which negatively impairs work performance and life quality. Hedysarum Multijugum Maxim (HMM) has been widely used for preventing and treating rhinitis. However, the pharmacologic effects of HMM and its mechanisms against rhinitis have not been fully studied. The bioactive compounds of HMM were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). The targets genes of HMM for treating rhinitis were identified using PharmMapper and GeneCards database integration. Protein-protein interaction analysis (PPI) was carried out using the GeneMANIA database. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were carried out using clusterProfiler. The drug-target-disease-GO-KEGG networks were built via Cytoscape. A total of 14 bioactive ingredients were screened and 25 target genes of HMM acting on rhinitis were discovered. PPI analysis demonstrated that these genes and their related genes had physical interactions or displayed co-expression characteristics. GO, KEGG and network analyses demonstrated that these targets were closely related to epithelial cell proliferation, regulation of inflammatory response, endocrine resistance, MAPK, VEGF, and TNF signaling pathways. In short, HMM displays multi-compounds, multi-targets to exert systematic pharmacologic actions on rhinitis.
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Introduction: Time-dependent effects of laser radiation have been investigated by researchers. An understanding of the molecular mechanism of the time course effect of the laser needs molecular assessment and function evaluation of the related genes. In the present study, the importance of repetition of treatment after 4 weeks and gene expression alteration after 7 days of laser radiation versus one day on the human skin was evaluated via protein-protein interaction (PPI) network analysis and gene ontology enrichment. Methods: The differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus (GEO) and assessed via PPI network analysis. The critical DEGs were enriched via gene ontology. The related biological processes and biochemical pathways were retrieved from "GO-Biological process" and "Kyoto Encyclopedia of Genes and Genomes" (KEGG) respectively. Results: The repetition of laser therapy after 4 weeks of the first treatment did not have a significant effect on treatment efficacy. Sixty-three significant DEGs and six classes of biological terms discriminated the samples seven days after the treatment from individuals one day after the treatment. The studied DEGs were organized into two clusters with certain functions. Conclusion: Based on the findings after laser therapy, several days are required to complete the critical processes such as DNA biosynthesis and skin cornification.
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Previous studies have demonstrated associations between cardiovascular disease and the expression of various messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). This study aimed to investigate the differential expression of mRNAs, lncRNAs, and miRNAs between tissues from patients with coronary artery disease (CAD) and healthy controls, and to determine the interactions between these molecules in CAD.We investigated the differential expression of competitive endogenous RNAs (ceRNAs) between patients with CAD and healthy controls by collecting data from Gene Expression Omnibus (GEO) microarrays. We also investigated the biological function of these differentially expressed ceRNAs by performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. We then created a protein-protein interaction (PPI) network to identify the hub genes. Biosystems and literature searches were also carried out to identify relevant signaling pathways and the potential function of the differentially expressed ceRNAs.We identified 456 expression profiles for miRNAs, 16,325 mRNA expression profiles, and 2,869 lncRNA expression profiles. With regards to connectivity, GO and KEGG analyses (count ≥9) identified the top 11 PPI network nodes in rank order. We also identified the top 15 significant nodes for the ceRNAs identified according to degree centrality (DC) (P<0.05). Collectively, our analyses confirmed that the differential expression of certain ceRNAs, and their respective signaling pathways were associated with CAD.Data arising from 11 GO and KEGG pathways, the top 15 PPI network nodes with the best connectivity rank, and the top 15 ceRNA network nodes, as determined by DC rank in CAD population, indicated that the differential expression of these ceRNAs plays a key role in the CAD. Our findings highlight new molecular mechanisms for CAD and provide new options for the development of therapeutic targets.
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Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein-protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein-protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
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To identify and predict the competing endogenous RNA (ceRNA) networks in colorectal cancer (CRC) by bioinformatics analysis.In the present study, we obtained CRC tissue and normal tissue gene expression profiles from The Cancer Genome Atlas project. Differentially expressed (DE) genes (DEGs) were identified. Then, upregulated and downregulated miRNA-centered ceRNA networks were constructed by analyzing the DEGs using multiple bioinformatics approaches. DEmRNAs in the ceRNA networks were identified in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using KEGG Orthology Based Annotation System 3.0. The interactions between proteins were analyzed using the STRING database. Kaplan-Meier survival analysis was conducted for DEGs and real time quantitative polymerase chain reaction (RT-qPCR) was also performed to validate the prognosis-associated lncRNAs in CRC cell lines.Eighty-one DElncRNAs, 20 DEmiRNAs, and 54 DEmRNAs were identified to construct the ceRNA networks of CRC. The KEGG pathway analysis indicated that nine out of top ten pathways were related with cancer and the most significant pathway was "colorectal cancer". Kaplan-Meier survival analysis showed that the overall survival was positively associated with five DEGs (IGF2-AS, POU6F2-AS2, hsa-miR-32, hsa-miR-141, and SERPINE1) and it was negatively related to three DEGs (LINC00488, hsa-miR-375, and PHLPP2). Based on the STRING protein database, it was found that SERPINE1 and PHLPP2 interact with AKT1. Besides, SERPINE1 can interact with VEGFA, VTN, TGFB1, PLAU, PLAUR, PLG, and PLAT. PHLPP2 can interact with AKT2 and AKT3. RT-qPCR revealed that the expression of IGF2-AS, POU6F2-AS2, and LINC00488 in CRC cell lines was consistent with the in silico results.CeRNA networks play an important role in CRC. Multiple DEGs are related with clinical prognosis, suggesting that they may be potential targets in tumor diagnosis and treatment.
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